Human motions have many subtleties that may make modeling of the motion difficult, if not impossible within a reasonable time frame. For example, because both space and time may factor into the model of a given gesture, the population whose gestures need to be detected may likely have large differences in how fast the gestures are executed. Factoring these variations into the model may introduce uncertainty, which may reduce the reliability of gesture detection decisions. Moreover, traditional detection algorithms may perform a time-consuming search for potential start and end points of the gesture of interest, which may slow performance and make the detection of back-to-back gestures (e.g., continuous detection) infeasible. Simply put, the processing time of conventional gesture learning and detection systems may be prohibitive for real-time systems.